Retrieval-augmented generation

Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.

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arXiv AI

Jun 19

QueryGaussian: Scalable and Training-Free Open-Vocabulary 3D Instance Retrieval

arXiv:2606. 19733v1 Announce Type: cross Abstract: Efficiently retrieving specific 3D instances from large-scale scenes via natural language prompts remains a formidable challenge in multimedia analysis.

By Xiuyuan Zhu, Ke Lu, Zijie Yang, Chao Yue, Jian Xue, Dongming Zhang

rag computer-vision efficiency benchmarks

arXiv AI

Jun 19

Policy-aware Vector Search: A Vision for Fine Grained Access Control in Vector Databases

arXiv:2606. 19803v1 Announce Type: cross Abstract: Vector databases are increasingly used in security sensitive contexts with Retrieval Augmented Generation and organizational AI pipelines; however, their security capabilities remain limited.

By Lakshmi Sahithi Yalamarthi, Primal Pappachan

rag

arXiv Machine Learning

Jun 19

Multimodal Concept Bottleneck Models

arXiv:2606. 19882v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of deep learning networks by aligning the features extracted from images with natural concepts.

By Tongqing Shi, Ge Yan, Tuomas Oikarinen, Tsui-Wei Weng

rag multimodal benchmarks safety

arXiv Machine Learning

Jun 19

Train, Retrieve, or Both? A Four-Arm Head-to-Head for Correct Statutory Citation on the Ontario Residential Tenancies Act

arXiv:2606. 20359v1 Announce Type: new Abstract: Self-represented tenants, landlords, and help-desk staff need to be pointed at the provision of law that actually governs a question, with a correct statutory citation.

By Ali Asaria, Tony Salomone, Deep Gandhi

rag fine-tuning safety

arXiv AI

Jun 19

Multi-Agent Transactive Memory

arXiv:2606. 19911v1 Announce Type: new Abstract: The decentralized deployment of LLM agents with diverse capabilities across diverse tasks motivates infrastructure for knowledge sharing across heterogeneous agent populations.

By To Eun Kim, Xuhong He, Dishank Jain, Ambuj Agrawal, Negar Arabzadeh, Fernando Diaz

llms rag agents

arXiv Machine Learning

Jun 19

Probe-and-Refine Tuning of Repository Guidance for Coding Agents

arXiv:2606. 20512v1 Announce Type: cross Abstract: LLM-based coding agents need higher-level operational knowledge about a repository (which files house which subsystems, how to run the test suite, which workflows have historically led to wrong fixes) that does not exist in the code itself.

By Asa Shepard, Jeannie Albrecht

llms rag agents

arXiv Machine Learning

Jun 19

On the Redundancy of Timestep Embeddings in Diffusion Models

arXiv:2606. 20416v1 Announce Type: new Abstract: Diffusion models rely heavily on explicit timestep embeddings to modulate the denoising process across various noise scales.

By Jos\'e A. Ch\'avez

llms rag diffusion benchmarks

arXiv Machine Learning

Jun 19

Exploring the potential of AlphaEarth and TESSERA embeddings for Fine-scale Local Climate Zone Mapping: A case study across five cities in Switzerland

arXiv:2606. 20034v1 Announce Type: new Abstract: Understanding urban spatial morphology is critical for climate modeling, risk assessment, and sustainable urban design, and Local Climate Zone (LCZ) mapping provides the basic framework for this.

By Htet Yamin Ko Ko, Clement Atzberger

rag data-engineering

arXiv AI

Jun 19

Boundary Embedding Shaping with Adaptive Contrastive Learning for Graph Structural Disentanglement

arXiv:2606. 20283v1 Announce Type: cross Abstract: Graph neural networks (GNNs) excel at aggregating neighbor information for classification, yet their performance is hindered by graph structural entanglement, where spurious correlations from semantically irrelevant neighbors contaminate node embeddings.

By Jiaqing Chen, Zidu Yin, Yichao Cai, Yuhang Liu, Zhen Zhang, Dong Gong, Javen Qinfeng Shi

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arXiv AI

Jun 19

Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks

arXiv:2606. 19489v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) enhance interpretability by projecting learned features into a human-understandable concept space.

By Ya Wang, Adrian Paschke

llms rag multimodal benchmarks safety

arXiv AI

Jun 19

Modularity-Free Conflict-Averse Training for Generalized PINNs

arXiv:2606. 20156v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have become a powerful framework for solving PDEs by embedding physical laws into differentiable objectives.

By Heejo Kong, Beomchul Park, Sung-Jin Kim, Seong-Whan Lee

rag efficiency benchmarks

arXiv Machine Learning

Jun 19

Spectral Retrieval-Augmented Time-Series Forecasting

arXiv:2606. 19412v1 Announce Type: new Abstract: Time series forecasting leverages historical patterns to predict future values, but traditional methods face challenges when dealing with complex, non-stationary patterns that are difficult to memorize during training.

By Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen, Hung Le

rag benchmarks

arXiv AI

Jun 19

REVEAL++: Differentiable Phenotypic Grouping for Vision-Language Retinal Modeling of Alzheimer's Disease Risk

arXiv:2606. 19522v1 Announce Type: new Abstract: The retina offers a noninvasive window into neurodegenerative disease, capturing subtle structural patterns associated with a risk of future cognitive decline.

By Ethan Elio Meidinger, Seowung Leem, Zeyun Zhao, Ruogu Fang

rag multimodal safety

arXiv AI

Jun 19

SIGMA: Search-Augmented On-Demand Knowledge Integration for Agentic Mathematical Reasoning

arXiv:2510. 27568v2 Announce Type: replace Abstract: Solving mathematical reasoning problems requires not only accurate access to relevant knowledge but also careful, multi-step thinking.

By Ali Asgarov, Umid Suleymanov, Aadyant Khatri

rag agents benchmarks

arXiv Machine Learning

Jun 19

Physics-Informed Neural Network with Squeeze-Excitation-like Attention

arXiv:2606. 19853v1 Announce Type: new Abstract: We introduce SEA-PINN, a novel architecture that incorporates a Squeeze-Excitation-like attention mechanism into physics-informed neural networks to dynamically recalibrate the importance of neurons across layers.

By Yun-Fei Song, Long-Gang Pang, Fu-Peng Li, Jun-Jie Zhang

rag benchmarks

arXiv Machine Learning

Jun 19

Multi-Granular Attention-Driven Reinforcement Learning Framework for Web Intelligent Enhancement Systems

arXiv:2606. 19690v1 Announce Type: new Abstract: From the past few years, web intelligent enhancement systems increasingly rely on heterogeneous and dynamic web data to deliver personalized, context-aware services.

By Navin Chhibber, Deepak Singh, Anokh Kishore, Nikita Chawla, K. Anguraj

rag agents reinforcement-learning

arXiv Machine Learning

Jun 19

Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings

arXiv:2104. 08928v4 Announce Type: replace-cross Abstract: Unstructured text provides decision-makers with a rich data source in many domains, ranging from product reviews in retail to nursing notes in healthcare.

By Kan Xu, Xuanyi Zhao, Hamsa Bastani, Osbert Bastani

rag nlp fine-tuning benchmarks

arXiv AI

Jun 19

Hybrid ANN-SNN Pipeline with Local Plasticity

arXiv:2606. 20151v1 Announce Type: cross Abstract: This work proposes a hybrid ANN-SNN pipeline that effectively leverages the rich embeddings of pretrained artificial neural networks (ANNs) to enable high-performance spiking neural networks (SNNs).

By Denis Larionov, Khairutin Shtanchaev, Mikhail Kiselev, Mikhail Korovin, Ivan Tugoy

rag benchmarks

arXiv AI

Jun 19

Bring My Cup! Personalizing Vision-Language-Action Models with Visual Attentive Prompting

arXiv:2512. 20014v3 Announce Type: replace-cross Abstract: While Vision-Language-Action (VLA) models generalize well to generic instructions, they struggle with personalized commands such as "bring my cup," where the robot must act on one specific instance among visually similar objects.

By Sangoh Lee, Sangwoo Mo, Wook-Shin Han

llms rag robotics multimodal benchmarks

arXiv Machine Learning

Jun 19

Prior-Informed Flow Matching for Graph Reconstruction

arXiv:2601. 22107v2 Announce Type: replace Abstract: We introduce \textit{Prior-Informed Flow Matching (PIFM)}, a conditional flow model for graph reconstruction.

By Harvey Chen, Nicolas Zilberstein, Santiago Segarra

rag diffusion benchmarks

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